Generate Image

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the asynchronous submission behavior, the need to poll the returned request_id, and detailed model-specific behaviors including error conditions ('passing them returns an error') and the exact-size exception for aspect_ratio:'custom'. Annotations provide only basic hints (readOnlyHint=false, etc.), so this context is valuable beyond what annotations offer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear one-line purpose, followed by an async note and a structured argument list. It is long but every section covers distinct parameter semantics or model-specific interactions; the bullet list improves scannability. Minor redundancy with schema descriptions keeps it from being maximally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with no output schema, the description covers all arguments, model families, default behavior, error conditions, and the follow-up polling mechanism via xbrush_get_request. It also provides concrete examples for the most complex parameter (aspect_ratio), making it exceptionally complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema covers 100% of parameters, the description adds meaningful semantics by grouping models into megapixel-based vs resolution-based, clarifying which parameters apply to which model types, and explaining the special 'custom' aspect_ratio behavior with a concrete example. This substantially aids correct parameter selection beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Generate images from a text prompt using XBrush AI models,' clearly identifying the operation and resource. However, it does not explicitly differentiate from siblings like xbrush_image_edit or xbrush_image_upscale, relying on the tool name and phrase 'from a text prompt' for implicit distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context (text-to-image generation, async flow with xbrush_get_request) and points to xbrush_list_models for model selection, but it never states when to prefer this tool over alternatives or when not to use it. Usage is implied rather than explicitly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.